Global transcriptome modulation by xenobiotics: the role of alternative splicing in adaptive responses to chemical exposures

Andrew J Annalora; Jacki L Coburn; Antony Jozic; Patrick L Iversen; Craig B Marcus

doi:10.1186/s40246-024-00694-6

. 2024 Nov 18;18:127. doi: 10.1186/s40246-024-00694-6

Global transcriptome modulation by xenobiotics: the role of alternative splicing in adaptive responses to chemical exposures

Andrew J Annalora ^1,^✉, Jacki L Coburn ¹, Antony Jozic ¹, Patrick L Iversen ¹, Craig B Marcus ¹

PMCID: PMC11572221 PMID: 39558396

Abstract

Background

Xenobiotic exposures can extensively influence the expression and alternative splicing of drug-metabolizing enzymes, including cytochromes P450 (CYPs), though their transcriptome-wide impact on splicing remains underexplored. This study used a well-characterized splicing event in the Cyp2b2 gene to validate a sandwich-cultured primary rat hepatocyte model for studying global splicing in vitro. Using endpoint PCR, RNA sequencing, and bioinformatics tools (rSeqDiff, rMATs, IGV), we analyzed differential gene expression and splicing in CYP and nuclear receptor genes, as well as the entire transcriptome, to understand how xenobiotic exposures shape alternative splicing and activate xenosensors.

Methods

Primary rat hepatocytes in sandwich culture were exposed to two methylenedioxybenzene (MDB) congeners and carbamazepine, with gene expression and splicing assessed. A 3D-clustergram integrating KEGG pathway analysis with differential gene expression provided distinct splicing landscapes for each xenobiotic, showing that splicing diversity does not always align with gene expression changes.

Results

Endpoint PCR revealed a Cyp2b2v to wild-type Cyp2b2 splicing ratio near 1:1 (100%) under most conditions, while RNA-seq showed a stable baseline closer to 40%. C6-MDB reduced this ratio to ~ 50% by PCR and ~ 39% by RNA-seq, showing slight method-dependent variations yet consistent trends. In contrast, exon 6 skipping in Cyp1a1 occurred only with MDB exposure, implicating AHR activation. Xenobiotic treatments also induced alternative splicing in defensome and stress-responsive genes, including the phase II enzyme Gstm3, Albumin, Orm1, and Fxyd1, highlighting their roles in xenobiotic response modulation. Significant splicing changes in factors such as SRSF1, SRSF7, and METTL3 suggest a coordinated feedback loop involving epitranscriptomic modulation and cross-talk within SR protein networks, refining splice site selection, transcript stability, and cellular fate.

Conclusions

This study demonstrates how xenobiotic structural features influence gene expression and splicing, revealing splicing patterns that expand our understanding of transcriptome diversity and function. By identifying regulatory mechanisms, including AHR activation, epitranscriptomic modulation, and crosstalk within SR protein networks, that shape adaptive responses to xenobiotic stress, this work offers insights into the splicing and transcriptional networks that maintain cellular homeostasis. These findings provide predictive biomarkers for toxic exposures and highlight the potential of splicing profiles as diagnostic tools for assessing the health impacts of chemical exposure.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-024-00694-6.

Keywords: Cytochromes P450, CYPs, Cytochrome P4502B2, Cyp2b2, Methylenedioxybenzenes, Hepatocytes, RNAseq, Alternate pre-mRNA splicing, Differential Gene expression, Alternative splicing, Alternative gene splicing, Nuclear receptors, Xenosensors, Epigenetics, Epitranscriptomics, Defensome

Introduction

Xenobiotics, including environmental pollutants and small-molecule drugs, alter gene expression extensively, yet their specific impact on splicing mechanisms varies across compounds and biological contexts. Alternative splicing, essential for generating protein diversity, is sensitive to environmental shifts triggered by xenobiotics like 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) [1]. Xenobiotics may directly interact with spliceosomal components, alter splicing factor expression, or affect splicing through cellular signaling and stress responses, leading to changes in the recognition and usage of alternative exons [2]. Nearly all human genes undergo alternative splicing, and disruptions in splicing due to xenobiotics or environmental factors contribute to diseases, including cardiovascular, neurodegenerative, and cancer-related disorders [3]. Understanding how gene-environment interactions influence splicing is a key focus in toxicology. This study examines the effects of specific xenobiotics, particularly methylenedioxybenzene (MDB) compounds and carbamazepine (CBZ), on splicing and gene expression in primary rat hepatocytes (PRHs), advancing our knowledge of xenobiotic impacts on hepatocellular function.

Methylenedioxybenzene (MDB) compounds, defined by their distinctive 1,3-benzodioxole structure (CAS 274-09-9), occur in both natural and synthetic sources. They are found in spices, such as piperine in black pepper [4], myristicin in nutmeg [5], safrole in sassafras [6], sesamol in sesame oil [7]. MDBs are also used as synergistic additives in insecticides, like piperonyl butoxide [8]. MDB structures are also present in naturally occurring toxins, including aristolochic acid, a genotoxic carcinogen [9, 10] and justicidin B, and justicidin B, an ichthyotoxin with anti-cancer and antimicrobial properties [11]. MDBs are the active compounds in many legal drugs such as piribedil (a dopamine receptors antagonist for Parkinson’s), tadalafil (a PDE5 inhibitor for erectile dysfunction, Cialis), etoposide (a topoisomerase II inhibitor for cancer), stiripentol (a GABA receptor enhancer used for epilepsy, Diacomit) and paroxetine (a selective serotonin reuptake inhibitor, or SSRI, for depression, Paxil) [12]. MDBs are also used in experimental cancer therapies, including omacetaxine (aminoacyl-tRNA inhibitor for leukemia) and saracatinib (src and bcr/abl kinase inhibitor for leukemia and solid tumors) [13]. They inhibit TGFβ mRNA [14], inhibit cell proliferation, and induce apoptosis via m-TOR and p-AKT pathways [15]. Some MDBs act as anti-inflammatory agents by inhibiting COX-2 and TNFα [16], and they are also found in recreational drugs like methylenedioxymethamphetamine (MDMA or ecstasy) [17]. The diverse biological activities of MDB compounds and their abundance in natural and synthetic forms underscore the need for a deeper understanding of their regulatory impacts.

This study examines transcriptome-wide splicing regulation, emphasizing genes involved in drug metabolism and disposition. For quality control in our in vitro system, we focused specifically on CYP splicing events. Structure-activity studies of MDB compounds have identified key determinants affecting CYP specificity, inhibition, and induction [8, 18, 19]. Studies reveal two main MDB metabolic pathways mediated by CYP enzymes, producing catechol and formic acid as main products [20, 21]. SAR studies reveal that MDBs interact with CYP enzymes to form a ferrous complex, showing absorption peaks at 427 and 455 nm. This interaction is influenced by alkoxy group size and lipophilicity: short (1–3 carbon) chains yield unstable complexes, while longer chains are stable. The MDB-CYP complex functions as a noncompetitive CYP inhibitor, forming a carbene-iron complex during catalysis [22].

While the structural determinants of MDB interactions with CYP enzymes are increasingly understood, less is known about their effects on CYP gene regulation at the RNA level, particularly regarding alternative splicing. This gap is especially relevant in xenobiotic-inducible CYPs, like Cyp1a1, where splicing changes may impact enzyme function and drug metabolism [1]. However, little is known about how xenobiotics influence alternative splicing of liver CYPs, including those in the rat model, where specific genes, like Cyp2b1, Cyp2b2, and the Cyp2b2 splice variant (Cyp2b2v), are affected [18, 23]. These Cyp2b isoforms are induced to varying degrees by structurally diverse MDBs (such as C1-MDB and C6-MDB) [24]. The splicing of Cyp2b genes is influenced by key regulators of pre-mRNA splicing, such as CDC2-like kinases (CLK) and dual-specificity tyrosine-regulated kinases (DYRK), which phosphorylate SR proteins [25]. Certain MDB compounds, including 6-arylquinazolin-4-amine, ML315, and marine-derived leucettamine B and leucettine L41, act as inhibitors of CLK1, DYRK1A, and DYRK2 [26, 27]. Regulation of Cyp2b2v expression and splicing provides insight into signaling pathways involved in alternative splicing, with C1-MDB and C6-MDB (1- and 6-carbon alkyl chains) serving as related yet contrasting tools for investigation.

In higher organisms, genes contain both coding exons and noncoding introns, and pre-mRNA splicing rearranges these segments to produce distinct mRNA transcripts and protein variants [28, 29]. Alternative splicing generates protein diversity essential for tissue-specific gene expression, development, and cellular adaptation. However, disruptions in splicing patterns are implicated in diseases such as cancer and genetic disorders [30–32]. Our recent investigation into the human CYP superfamily [33] and nuclear hormone receptors [34] revealed that each of the 57 CYP and 48 NR genes responds to distinct splicing cues, with each gene expressing an average of ~ 20 unique mRNA transcripts. While many of these CYP and NR variants are translated into proteins with the potential for both beneficial and toxic effects, their full coding and non-coding functions across various tissues, along with the primary drivers of their induction, remain unclear.

Environmental pollutants, drugs, hormones, and toxicants can disrupt splicing, altering gene expression and protein function. Some compounds, such as D-erythro-C6 and D-erythro-C18-ceramides, interact directly with splicing factors, decreasing SR-protein phosphorylation and modifying splicing patterns, including changes to Bcl-x splicing [35–37]. The short chain D-e-C6-ceramide changes Bcl-x alternate splicing [36, 37]. Other compounds influence cellular signaling pathways. For example, dexamethasone induces exon skipping in the ATM gene, affecting over 891 transcripts [38, 39]. Vitamin D hormones also modulate CYP24A1 splicing in a tissue-specific manner, potentially through nuclear receptor interactions, producing variants with altered mitochondrial targeting and membrane binding [40–42]. Additionally, some chemicals, particularly histone deacetylase (HDAC) inhibitors, induce epigenetic changes that affect splicing. Notable examples include Sodium Butyrate (NaB), Trichostatin A (TSA), and Vorinostat (SAHA), which alter histone acetylation and influence splice site selection and exon inclusion in numerous genes [43, 44].

Previous observations that xenobiotic exposure induces differential alternative splicing in the Cyp2b2 gene [23] were pivotal for this project. The Cyp2b2 variant (Cyp2b2v) is a 24-base extension of exon 5, formed by shifting the 5’-splice site on intron 5 (Supplementary Fig. 1; Additional Materials). Pre-mRNA splicing regulation is complex, but most alternative splice sites in the human genome are within 6 nucleotides of the dominant splice site. This proximity emphasizes the need to monitor small splice variants like Cyp2b2v, which may be sensitive to disruptions in splicing regulation. Interference with cis-acting elements and trans-acting factors, such as U1 snRNP binding to the 5’-splice site and U2AF1/U2AF2 binding to the 3’-splice site, can shift splice site recognition, potentially affecting the expression of such variants [45, 46]. Aryl sulfonamides and phenothiazines can interfere with splice site recognition, disrupting normal splicing and altering alternative splicing outcomes [47]. This disruption affects splicing enhancer sequences (ESE/ISE) recognized by serine/arginine-rich (SR) proteins and silencer sequences (ESS/ISS) recognized by heterogeneous nuclear ribonucleoproteins (hnRNPs), which can redefine exon junctions [48, 49]. Antisense splice-switching oligonucleotides have been employed to disrupt SR protein interactions with ESE/ISE for treatment of Duchenne Muscular Dystrophy [50, 51]. Additionally, we observed that RNA guanine quadruplexes (rG4) can shift splicing from the cryptic splice acceptor in intron 3 of the CYP3A5*3 polymorphism [52].

Earlier studies also showed MDB compounds with 6-carbon side chains were associated with transcriptional induction of Cyp2b1 and Cyp2b2 [23], while the catechol derivative of C6-MDB does not, highlighting the importance of the methylenedioxy bridge for transcriptional induction [18]. Although the exact mechanisms of C6-MDB-induced splicing remain unclear, it is known that an unsubstituted MDB methylene carbon is essential for forming a stable complex with CYPs [53]. Furthermore, compounds that disrupt RNA polymerase II elongation, such as 5,6-dichloro-1-b-D-ribofuranosyl-benzimidazole (DBR), can lead to alternative splicing [54]. Ligand interactions with nuclear receptors can also influence splicing, as evidenced by the effects of AHR activation by TCDD [1]. Additionally, histone deacetylase activity plays a significant role in splicing regulation, affecting exon inclusion [55].

The complex interplay of alternative splicing regulation and the diverse actions of MDB compounds complicates our understanding of splicing variations among various xenobiotics, including C1-MDB, C6-MDB, and CBZ. In our global splicing analysis, we investigated splicing changes in various highly modulated genes, with a specific focus on Cyp2b genes, which we assessed using PCR due to their well-documented profiles and their role as a quality control in our hepatocyte model. Although Cyp1a1 is known to be sensitive to xenobiotic-induced changes in splice variant expression, we limited our evaluation to RNA-seq data rather than PCR, as its splicing is less well characterized than Cyp2b2 in the rat hepatocyte model. We also explored how the differing side chain lengths of C1-MDB and C6-MDB may affect their interactions with CYPs and xenosensors like the aryl hydrocarbon receptor (AHR), aiming to uncover potential mechanisms underlying these splicing changes through pathway analysis. Additionally, we examined CBZ, a non-MDB mixed CYP inducer with pharmacological relevance, to provide a benchmark for understanding the differential impacts of MDBs versus traditional CYP inducers on splicing mechanisms and CYP regulation.

Despite advancements, splicing data analysis is hindered by issues related to data quality, quantity, integration, and biological complexity, as well as limitations in algorithms and a lack of standardized formats for interpreting patterns [56–58]. A significant challenge in our study was developing a robust pipeline to analyze splicing changes, as many programs effectively identify global alterations but struggle to detect smaller events like the expression of Cyp2b2v. This research explores how structurally diverse xenobiotics disrupt drug metabolism and disposition through splicing variations, which may contribute to drug-drug interactions and influence changes in metabolic gene expression linked to environmental diseases. Additionally, we examine broader changes in the splicing landscape to identify other gene families involved in these processes, while also seeking to understand the nuclear receptor crosstalk that regulates these complex interactions.

Results

Validation of alternative splicing in the xenobiotic-induced PRH model

Before initiating a global splicing analysis, we aimed to validate our in vitro model system by confirming the expression and splicing of known in vivo Cyp2b2 variants induced by xenobiotics. This step was essential for establishing physiological relevance and ensuring our model’s reliability for downstream transcriptome-wide splicing studies. In this study, we focused on recapitulating the xenobiotic-induced expression and splicing patterns of the Cyp2b gene family in vivo [18], specifically the Cyp2b2v splice variant. This focus serves as a quality control measure for our primary rat hepatocyte (PRH) model, providing physiological relevance to our global splicing analysis. The Cyp2b2v variant includes a small addition of eight amino acids (VSPAWMRE), resulting from a cryptic 5’-splice site in intron 5. Detailed structural and sequence information regarding the Cyp2b2 gene and CYP2B2v protein is available in the Additional Materials (Supplemental Fig. 1). To achieve our goals, we compared the bioactivity of three discrete xenobiotics (Fig. 1), focusing on their targeted effects on Cyp2b2v splicing and broader impacts on the global transcriptome. This analysis included comparisons of two MDB compounds (C1-MDB and C6-MDB) and carbamazepine (CBZ), each known to be mixed inducers of multiple CYP enzymes from CYP Families 1–3 [18, 19, 59].

Here, we developed a semiquantitative endpoint PCR assay for rat Cyp2b1, Cyp2b2, and Cyp2b2v, multiplexed with the SDHA housekeeping gene, to validate the basal expression level and inducibility of the Cyp2b2v splice variant transcript in PRHs grown in sandwich culture. Because some in vitro hepatocyte cultures fail to replicate in vivo expression or splicing [60], confirming Cyp2b2v splice variant expression served as a quality control for our global splicing analysis, as its absence could indicate broader issues with splicing machinery in our system. To detect this variant, primers were designed to target non-homologous regions of the Cyp2b2v mRNA, which differs from the wild-type by only 24 bp, resulting in distinct amplicon sizes of each gene (Supplemental Table 1; Additional Materials).

Test concentrations for the three xenobiotics (Fig. 1) and the vehicle (DMSO; 0.5-1%) were based on published studies [18] and PRH toxicity dose-response profiling (100–500 µM), as highlighted in Supplemental Figs. 2 and 3 (Additional Materials). Optimal treatment (100–250 µM) and vehicle control (0.05%) levels were selected based on pilot studies of fresh PRHs in a 24-well, sandwich culture format. A modest, but insignificant difference in Cyp2b2v mRNA was detected at 1% DMSO, leading us to select 0.5% as the optimal vehicle level for these studies (Supplemental Fig. 4). Multiplexed endpoint PCR analysis of Cyp2b1, Cyp2b2, and Cyp2b2v gene expression showed clear separation of amplified PCR bands: Cyp2b1 (110 bp), Cyp2b2 (394 bp), and Cyp2b2v (843 bp), from the SDHA housekeeping gene (218 bp). These distinct and diagnostic bands enable semi-quantitative comparisons of CYP expression across treatment groups using CYP/SDHA ratios. (Fig. 2a). Treatment of PRHs (24 h) with 250 µM C1-MDB C1 did not significantly alter basal Cyp2b1 mRNA expression levels, similar to vehicle (0.5% DMSO). However, treatment with 250 µM C6-MDB resulted in an approximately two-fold increase in Cyp2b1 transcript (*p < 0.05). Treatment with 100 µM CBZ resulted in an increase in Cyp2b1 message that was significantly increased from both vehicle (**p < 0.005) and C6 MDB (*p < 0.05) as shown in Fig. 2b.

Fig. 2 — Endpoint PCR Analysis of Cyp2b1, Cyp2b2 and Cyp2b2v mRNA Expression in Fresh Sprague-Dawley PRHs. **(a)** Results of semiquantitative, endpoint PCR using fresh PRHs grown in sandwich culture, for rat CYPs 2b1, 2b2, and 2b2v. The succinate dehydrogenase (SDHA) gene was selected as the optimal housekeeping gene in this study, as compared to ACTB and GAPDH (not shown). **(b)** CYP2B1 mRNA levels were normalized to SDHA and quantified using Image J software analysis using 3 replicate images. Statistically significant changes are denoted (** indicates p < 0.01; Student T-test) and standard deviation is shown. **(c)** Cyp2b2 WT and **(d)** Cyp2b2v mRNA levels were also normalized and quantified in a similar way using Image J analysis. Statistically significant changes (* indicates p > 0.05, ** indicates p < 0.01, *** indicates p < 0.005) are denoted and standard deviation is shown for each treatment group

Expression levels of mRNA encoding rat Cyp2b2 were unchanged comparing untreated and vehicle treated (0.5% DMSO) PRHs. Treatment with 250 µM C1-MDB resulted in a significant, nearly 5-fold, increase in wild-type Cyp2b2 message (***p < 0.001) compared to 0.5% DMSO vehicle control. Treatment with 250 µM C6-MDB significantly increased Cyp2b2 transcript over vehicle (****p < 0.0001) nearly 10-fold, and over C1-MDB (*p < 0.05) over 2-fold. Similarly, 100 µM CBZ treatment induced a significant, near 15-fold, increase in wild-type Cyp2b2 mRNA over vehicle (****p < 0.0001), and an approximate 3-fold increase in C1 MDB treatments (*p < 0.05) as shown in Fig. 2c.

With respect to the Cyp2b2v splice variant, vehicle treated (0.5% DMSO) and untreated PRHs did not differ significantly in Cyp2b2v transcript level. Treatment with 250 µM C1-MDB resulted in a significant ~ 3-fold increase in Cyp2b2v message (**p < 0.005) compared to vehicle control. Both 250 µM C6-MDB and 100 µM-CBZ treatment induced Cyp2b2v mRNA by over 10-fold compared to the vehicle control (****p < 0.0001) and over 3-fold compared to 250 µM C1 MDB (**p < 0.005) (Fig. 2d).

The mRNA expression ratios of wild-type Cyp2b1 to Cyp2b2v and wild-type Cyp2b2 to Cyp2b2v were quantified using endpoint PCR. As detailed in Supplemental Fig. 5 (Additional Materials), Cyp2b2v expression was consistently high, reaching approximately 90 to 100% of wild-type Cyp2b2 levels across all controls (Untreated, 0.88 ± 0.46; Vehicle, 0.94 ± 0.46) and most treatment groups (C1-MDB, 0.92 ± 0.45; CBZ, 0.98 ± 0.50). However, C6-MDB exposure resulted in a two-fold reduction in Cyp2b2v levels (0.55 ± 0.44). The Cyp2b1 to Cyp2b2v ratio, initially 12–15 times higher in controls (Untreated, 12.71 ± 2.83; Vehicle, 15.55 ± 3.91), decreased substantially upon treatment: C1-MDB (3.76 ± 1.30), CBZ (3.49 ± 1.08), and most notably C6-MDB (1.62 ± 0.17). This reduction indicates that although both Cyp2b1 and Cyp2b2 may be induced by xenobiotics, Cyp2b2 induction outpaces Cyp2b1. The pronounced decrease in the Cyp2b2v variant ratio to both Cyp2b1 and Cyp2b2 with C6-MDB suggests possible the involvement of a specific xenosensor (e.g., AHR, ) whose activation may function to alter Cyp2b gene transcript stability in a unique way. These findings underscore the complex regulatory interactions that modulate global splice variant production during xenobiotic exposures, and these results provide a reference point for comparing splicing ratios in RNA-seq analyses.

Global differential gene expression profiling of xenobiotic-induced PRHs using RNAseq

After validating the expression of CYPs 2B1, 2B2 and 2B2v in our PRH sandwich culture model, we conducted an Illumina-based RNAseq Analysis of global gene expression and splicing changes in PRHs treated with 250 µM C1-MDB, C6-MDB and 100 µM CBZ. Each treatment group (untreated, vehicle (0.05% and 1%), C1-MDB, C6-MDB, and CBZ) was sequenced in quadruplicate. Principal component analysis (PCA) of each biological replicate and treatment group showed strong correlation with limited outliers (Fig. 3a). A Pearson correlation plot further confirmed this, with high correlation (R² > 0.9) across all samples, except for one outlier (C6-MDB-2) shown in Supplemental Fig. 6a (Additional Materials). A Venn Diagram generated using the Comparative Toxicogenomics Database (CTD) [61] illustrates the full spectrum of differentially expressed genes across all treatments in our RNAseq analysis, including genes unique to each compound and those shared among each pair (Fig. 3b). We identified over 10,000 genes active in our four primary treatment groups (Vehicle (0.5% DMSO), C1-MDB, C6-MDB, and CBZ), as highlighted at the center of the Venn diagram, with each treatment modulating ~ 50 to 150 unique genes.

Fig. 3 — Global RNAseq Analysis of Differential Gene Expression in C1-MDB, C6-MDB, and CBZ-treated PRHs. **(a)** Principal Component Analysis (PCA) of RNA-seq data from untreated, control (VEH_05 and VEH_1), and treated (C1-MDB, C6-MDB, and CBZ) primary rat hepatocyte groups, showing strong clustering across groups. Notable outliers include C6-MDB replicate 2 along PC1 and variance in C1-MDB along PC2. To maintain a comprehensive dataset, all replicates were included in downstream analyses to capture the full scope of gene expression and splicing changes in the PRH model. **(b)** Venn diagram depicting the overlap of differentially expressed genes (DEGs), relative to control (VEH_05), across treatments (C1-MDB, C6-MDB, and CBZ) based on DESeq2 analysis. The central overlap represents over 10,000 commonly expressed genes, though not all are differentially regulated. Each xenobiotic modulated approximately 50 to 150 unique genes, indicating treatment-specific regulation, while the central set reflects genes expressed under all conditions. **(c)** Venn diagram from DEG analysis using rSeqDiff, identifying 58 genes commonly altered across all treatments (C1-MDB, C6-MDB, CBZ) compared to control. Unlike the DESeq2 analysis, the control (VEH_05) is not shown, focusing instead on shared gene changes between treatments. C6-MDB and C1-MDB showed more substantial gene expression changes (488 and 368 genes, respectively) compared to CBZ (182). CBZ and C1-MDB shared the highest number of commonly modulated genes (136), compared to CBZ and C6-MDB (88) or C1-MDB and C6-MDB (92). Detailed control comparisons are provided in Supplemental Tables 3–6 (Additional Materials)

Differentially expressed gene (DEG) analysis using rSeqDiff [62] and DESeq2 [63] produced nearly identical results. In the DESeq2 dataset, 58 genes were commonly differentially expressed (log2 fold change > ± 1) across all three treatments (C1-MDB, C6-MDB, and CBZ) compared to control (0.5% DMSO). C6-MDB induced the largest expression changes (488 genes), followed by C1-MDB (368 genes) and CBZ (182 genes). Notably, CBZ and C1-MDB shared the most commonly modulated genes (134), compared to CBZ and C6-MDB (88) or C1- and C6-MDB (91) (Fig. 3c). GO Biological Process enrichment analysis of the 58 common DEGs highlighted roles in oxidative stress regulation, cytokine production, and inflammation, with key genes like FMO1, NOS2, HSPA1B, NOX4, and DUOX1. These findings suggest coordinated regulation of NADPH oxidase activity and cytokine signaling, which are crucial defenses against xenobiotics and infections (Supplemental Fig. 6b; Additional Materials). Using the Transcription Factor (PPI) pathway analysis module on the Enrichr bioinformatics platform [64–66], which leverages a literature-based protein interaction map, we identified a network of transcription factors (IRF1, IRF6, NFKB1, MYC, HSF1, NR2F2 (COUP-TF2)) driving gene expression changes in response to xenobiotic exposure. Each transcription factor in this module is associated with a set of interacting proteins, illustrating potential regulatory connections within the network (Supplemental Fig. 6c; Additional Materials).

DESeq2 analysis identified the most differentially expressed genes across the three xenobiotic treatments, detailed in Supplemental Table 2 (Additional Materials). C1-MDB strongly induced genes (up to 220-fold; Adgre1) linked to detoxification pathways (Ugt2b7), immune responses (Adgre1, Ly6al), and tissue repair mechanisms (Lrg1, Nos2), while suppressing genes (up to 943-fold; Rps9) linked to protein synthesis (Rps9), growth regulation (Igfbp6), neuronal development (Nxpe3), immune signaling (Cd209), and potassium ion transport (Kcnk1). C6-MDB significantly induced genes (up to 136-fold; Cyp2b1) involved in xenobiotic metabolism (Cyp2b1, Cyp2b2, Cyp1a1, Cyp3a23-3a1) and RNA processing (Rpph1) and suppressed those related to DNA binding and transcription (Zkscan8, up to 22-fold), cell adhesion (Lamb2), non-coding RNA regulation (Thumpd3-as1), and receptor tyrosine kinase signaling (Ephb6). CBZ, more similar to C6-MDB than C1-MDB overall, significantly induced genes (up to 71-fold; CYP2B1) involved in xenobiotic metabolism (Cyp2b1, Cyp2b2, Cyp2c6v1), rRNA processing (Rn28s), and immune modulation (Lexm) while suppressing genes (up to ~ 1600-fold; Rps9) linked to steroid hormone metabolism (Hsd17b1), protein synthesis (Rps9), cytoskeleton regulation (Rac2), leucine-rich repeat containing (Lrrc36), and transcriptional repression (Hopx).

Exposure to all three xenobiotics significantly induced genes involved in xenobiotic metabolism, immune responses, and tissue repair, while suppressing genes related to protein synthesis, most notably Rps9. This suppression of Rps9 may indicate a strategic cellular shift under xenobiotic stress, prioritizing detoxification, damage control, and survival pathways as part of the defensome response [67]. KEGG Pathway analysis of all differentially expressed genes induced (Supplemental Table 1; Additional Materials) or suppressed (Supplemental Table 4; Additional Materials) by all 3 treatments (C1-MDB, C6-MDB and CBZ) confirmed these trends, with the pathway for “steroid hormone biosynthesis” occurring in both lists, highlighting its complex association with xenobiotic exposures like endocrine disrupting chemicals (EDCs). GO Biological Process enrichment (Supplemental Table 5; Additional Materials) and KEGG Pathway analysis (Supplemental Table 6; Additional Materials) were also conducted on the 58 common genes modulated by all 3 xenobiotics, revealing significant involvement in crucial cellular response pathways, including the superoxide metabolic process (DUOX1, NOS2, NOX4) and the inflammatory response (CXCL9, CEBPB, NOS2, NOX4), which were highly enriched to mitigate oxidative stress and inflammation. Furthermore, the regulation of cytokine production (HIC2, NOS2, IL15, IRF8) and the positive regulation of wound healing (DUOX1, SMOC2) highlight the complex interplay between immune modulation and tissue repair. KEGG pathway analysis also pointed to significant modulation within the Pertussis (NOS2, IRF8), IL-17 signaling pathway (CEBPB, FOSB), and TNF signaling pathway (CEBPB, IL15), illustrating the xenobiotics’ impact on immune signaling, with potential implications for host defense mechanisms.

Detailed KEGG pathway analysis of differentially expressed genes (DEGs) induced or suppressed by C1-MDB, C6-MDB, or CBZ, individually, reveals distinct biological impacts of these xenobiotics on pathways linked to metabolic detoxification, cytokine signaling and the immune response. (Supplemental Tables 7–12; Additional Materials). These findings are expanded upon in the Supplemental Results (Additional Materials), providing a deeper discussion on the specific genes and pathways modulated by each xenobiotic. In summary, C1-MDB’s modulation of gene expression centered on inflammatory responses and chemotaxis, potentially enhancing immune defense mechanisms. C6-MDB targeted cholesterol metabolism and steroid biosynthesis, suggesting impacts on lipid homeostasis and hormonal processes. CBZ modulated genes linked to cholesterol catabolism and wound response.

A comprehensive GO Biological Process Analysis of all significant (p < 0.05) DEGs modulated by C1-MDB, C6-MDB, and CBZ revealed distinct biological effects of each xenobiotic (Supplemental Tables 13–15; Additional Materials). The GO Biological Process findings analysis are further detailed in the Supplemental Results (Additional Materials), but overall, enriched GO terms aligned with the KEGG analysis, highlighting specific effects of each xenobiotic on immune response, metabolic processes, and tissue homeostasis. The distinct cellular effects of C1-MDB, C6-MDB, and CBZ stem from their roles as mixed inducers of metabolic genes, each activating a distinct array of xenosensors and signaling networks that drive unique transcriptional responses in inflammation, metabolism and tissue repair.

Volcano Plot Analysis of metabolic gene modulation in primary rat hepatocytes treated with vehicle control (0.5% DMSO), C1-MDB (250 Inline graphic M), C6-MDB (250 M), and CBZ (100 M) was performed using VolcaNoseR [68]. To provide a clear definition of “metabolic genes,” the primary Phase 0-III genes involved in xenobiotic disposition, as identified in our analysis, are listed in Supplemental Table 16 (Additional Materials). Differential expression profiles of significantly modulated (log2fold change > ± 1.5; p-value < 0.05) Phase 0 (NR genes, AHR related genes), Phase I (CYPs, FMO, etc.), Phase II (UGTs, GSTs, SULTs, etc.) and Phase III (SLC and ABC family transporters) genes were analyzed. Minimal transcriptional changes were observed for the vehicle control, with notable suppression of phase II (Gstm5) and phase III (Slc25a18) genes and induction of one Phase 0 (vitamin D receptor; Vdr) and one Phase III (Slc26a10) gene (Fig. 4a). In C1-MBD treated cells, differential expression of several Phase I-III metabolic enzymes was observed (Fig. 4b). This included significant induction of five Phase I enzymes, including CYPs 1a2, 2b1, 2b2, 2c6v1 and 4f37, along with one Phase II enzyme (Gsto2) and one phase III transporter (Slc26a24). Significant suppression was noted for the phase III transporter Slc22a13, as well as for Gstm5 and Slc25a18, which were also suppressed by the vehicle control (Fig. 4b). In contrast, C6-MDB treated primary rat hepatocytes resulted in stronger modulation of metabolic enzymes compared to C1-MDB. Differential expression of 13 CYP genes were detected in C6-MDB-treated cells, with notable induction of CYPs 1a1, 1a2, 2b1, 2b2, 2c6v1, 2c13, 3a23/3a1, 3a2, and 7a1, and suppression of CYPs 7b1, 8b1 and 46a1 (Fig. 4c). C6-MDB also induced significant differential expression in five phase II genes, with induction of Gsta2, Gsto2, Gsta5 and Gstt3, and suppression of Gstm5, which was suppressed in the control and all treatments except CBZ (Fig. 4c). C6-MBD treatments also strongly modulated phase III transporters and efflux pumps, inducing both ABC (Abcd2) and SLC family transporters (Slc4a1, Slc5a10, Slc16a5, Slc25a45, and Slco1a4) and suppressing one ABC transporter (Abc8a) and ten SLC transporters (Slc1a2, Slc1a5, Slc2a4, Slc2a9, Slc11a1, Slc13a5, Slc25a18, Slc34a2, Slc51a) (Fig. 4c). CBZ induced a similar set of phase I genes as the C6-MDB, with CYPs 2b1, 2b2, 2c6v1, 2c11, 2c13, 3a2, 3a23/3a1, and 7a1 (Fig. 4d). However, CBZ did not induce CYPs from the 1 A family (Cyps 1a1, 1a2). Additionally, CBZ suppressed several CYPs, including Cyp3a9 and the cholesterol metabolizing enzymes Cyp7b1 and Cyp8b1. Although Cyp8b1 fell slightly below our significance threshold (log2fc = -1.45) its concurrent suppression with Cyp7b1 mimics the response seen with C6-MDB, but not C1-MBD. In contrast, CBZ modulation of phase II enzymes differed from C6-MDB, with significant induction of Gsta5, Gsto2 and Ugt2b17, and no suppression of Gstm5. Unique to CBZ was the suppression of the phase III transporter Abcg1. CBZ also modulated 10 SLC transporters, inducing Slc5a10, Slc16a5, Slc25a45, and Slc26a5, while suppressing Slc2a6, Slc12a5, Slc13a2, Slc25a18, Slc26a9, and Slco1a2, showing only partial overlap with the phase III modulation by C6-MDB (Fig. 4d).

Fig. 4 — Volcano Plot Analysis of Metabolic Gene Modulation in Vehicle Control, C1-MDB, C6-MDB and CBZ-treated PRHs. The differential expression of important Phase 0 (NR genes), Phase I (CYP genes), Phase II (UGTs, GSTs, SULTs, etc.) and Phase III (SLC and ABC transporters) genes were analyzed across each treatment group and the vehicle control. **(a)** Compared to untreated PRHs, cells treated with 0.5% (v/v) DMSO (vehicle) showed minimal induction of metabolic enzymes, but we detected a modest, but significant upregulation (log2fold change > ± 1.5; p-value < 0.05) of the vitamin D receptor (Vdr; NR1i1). The vehicle control also significantly modulated the expression of one phase II enzyme (Gstm5) and two, phase III transporters (Slc26a10 and Slc25a18) **(b)** For C1-MBD treatments, significant differential expression of phase I CYPs (1a2, 2b1, 2b2, 2c6v1, and 4f37 (all upregulated)), Phase II conjugating enzymes (Gsta5 (downregulated), Gsto2 (upregulated), and Phase III transporters (Slc22a13 and Slc25a18; downregulated; Slc26a10 upregulated) were detected. **(c)** In contrast, C6-MDB induced stronger modulation of multiple metabolic enzymes. Differential expression of 13 CYP genes were detected for C6-MDB, including strong induction of CYPs 1a1, 1a2, 2b1, 2b2, 2c6v1, 2c11, 2c13, 3a23/3a1, and 7a1, along with suppression of CYPs 7b1, 8b1 and 46A1. C6-MDB also induced significant differential expression in several phase II genes, including Gsta2, Gsta5, Gsto2, Gstt3 (upregulated) and Gstm5 (downregulate), and multiple phase III transporter genes, some of which were strongly induced (e.g. Slc16a5), while others were significantly suppressed (e.g. Slc1a2 and Slc1a5). **(d)** CBZ induced a similar set of phase I genes as C6-MDB, with CYPs 2b1, 2b2, 2c6v1 and 3a2 being most strongly induced. Several CYPs were also suppressed by CBZ, including CYPs 7a1, 8b1, and Cyp3a9. CBZ also strongly induced several phase II enzymes, including Gsta5, Gsto2, and Ugt2b17, without suppressing any. CBZ also had major effects on phase III enzyme expression, inducing or suppressing 10 distinct SLC and ABC transporter genes

RNAseq analysis of alternative gene splicing in xenobiotic-induced PRHs

To quantify alternative splicing across all genes in our PRH sandwich culture model, we developed a bioinformatics pipeline incorporating rSeqDiff [62], rMATs-turbo (rMATs 4.1.2; [69, 70]) and the Integrated Genomic Viewer (IGV; [71–74]). Although alternative splicing changes were detected using rSeqDiff (model 2; T-value), this tool does not generate a false discovery rate (FDR), essential for high-confidence quantification of splicing events. The absence of FDR values can lead to false positives, where statistical anomalies might be misinterpreted as genuine biological changes. To address similar challenges, Luo et al. (2020) propose a two-step mixed model approach to improve the reliability of differential alternative RNA splicing detection by incorporating a FDR estimation method [75]. Despite these limitations, we compared rSeqDiff T-values (model 2; splicing changes) with differential gene expression values (model 1; log2fold-Δ data) to visualize global trends between gene expression and alternative splicing among all 3 treatment groups and vehicle (0.5% DMSO) (see Supplemental Fig. 7a-7d; Additional Materials). Based on rSeqDiff analysis only, C1-MDB exposure was associated with novel splicing events in genes with both increased and decreased expression, while C6-MDB and CBZ treatments showed more pronounced alternative splicing linked to gene induction than suppression.

To address the limitations of rSeqDiff, we used rMATs-turbo for our global gene splicing analysis, applying a 5% false discovery rate (FDR) as a threshold for significance. rMATs-turbo detects five types of alternative splicing events in RNAseq data: alternate 3’ spice site (A3SS), alternative 5’ splice sites (A5SS), skipped exon (SE), mutually exclusive exon (MXE), and retained intron (RI). The difference in PSI (percent splice in) values between the control (vehicle) and each xenobiotic treatment reflects changes in the frequency of individual splicing events. A negative PSI difference indicates increased alternative splicing events in the treatment group relative to the control.

We analyzed global RNA splicing changes using the same RNAseq dataset as the DEG analysis, with cumulative rMATs-turbo results shown in Fig. 5a and b. In PRHs treated with 250 µM C1-MDB, 68,554 total alternative splicing events were detected, of which 6,525 were significant (p < 0.05). Approximately 40% were skipped exons (SE) (27,056 total; 3,112 significant), 19% were A5SS (13,071 total; 930 significant), 29% were A3SS (19,832 total; 1,557 significant), 5% were MXE (3,681 total; 398 significant), and 7% were RI (4,914 total; 28 significant). Similar trends appeared for PRHs treated with 250 µM C6-MDB, with 66,056 total alternative splicing events being detected, with 4,945 significant events. Of these 37% were SE (24,616 total; 2,274 significant), 21% were A5SS (13,758 total; 731 significant), 29% were A3SS (19,028 total; 1,133 significant), 6% were MXE (3,792 total; 352 significant), and 7% RI (4,862 total; 455 significant). The 100 µM CBZ treatment produced the highest number of splicing events (77,035), although fewer were significant (5,906) compared to C1-MDB (6,525). Among these, 39% were SE (30,167 total; 2,773 significant), 20% were A5SS (15,010 total; 838 significant), 29% were A3SS (22,058 total; 1,265 significant), 6% were MXE (4,316 total; 352 significant), and 7% were RI (5,484 total; 455 significant).

An overview of the most commonly spliced genes (in terms of total events) and the most highly spliced genes (in terms of the largest ΔPSI) are summarized for each of the 3 treatments (C1-MDB, C6-MDB and CBZ) in Supplemental Tables 17–22 (Additional Materials). We observed significant overlaps, along with some unique responses, in alternative splicing across the 3 treatments, with albumin (alb) being the most commonly spliced gene among each of the 3 treatments (31 individual splicing events; C1-MDB) and cumulatively (53 total splicing events; across all 3 treatment groups). The splicing of alb and Orm1 are notably modulated across all three treatments, indicating their critical role in response to xenobiotic stress, with alb undergoing predominantly MXE and SE events, and Orm1 experiencing a greater variety of splicing changes. Orm1 is part of the lipocalin family and acts as a transport protein in the blood, similar to albumin. NXPE4, a gene involved in neural development, potentially as a receptor or adhesion molecule, was found to be the most commonly spliced gene after C6-MDB treatment (15 events; SE), and was not detected in the other treatment groups. Furthermore, genes like Gstm3 and Fxyd1 were affected by multiple treatments but displayed unique splicing events for each of the 3 exposures, highlighting the complexity and dynamic nature of the splicing process.

GO Biological Process enrichment analysis of 833 genes modulated (> 10% change in a splicing event) by the C1-MDB treatment (Supplemental Table 23 Additional Materials) reveals significant modulation in biological processes related to RNA dynamics and chromatin remodeling, including RNA- and miRNA-processing, mRNA stability, and chromatin remodeling, highlighting a widespread impact on the regulation of gene expression. Overlap genes in these categories, such as AGO4, TUT4, and PUM1 are involved in RNA silencing, mRNA decay, and post-transcriptional modulations, underscoring the complex regulation of RNA-binding proteins and their modifiers in response to xenobiotic stress.

GO Biological Process enrichment analysis of the 862 genes influenced (x > 10% in PSI) by the C6-MDB treatment (Supplemental Table 24; Additional Information) identified significant alterations in processes related to RNA splicing, processing, and metabolic regulation, showcasing the treatment’s profound impact on the RNA lifecycle. The inclusion of processes related to monocarboxylic acid biosynthesis and vesicle-mediated transport between the ER and Golgi apparatus indicates a broader cellular response, affecting a more complex matrix of metabolic pathways and intracellular trafficking events than the C1-MDB treatment. Overlap genes like SRPK2, MBNL1, and HNRNPH1, which regulate discrete aspects of splicing, underscore the intricate regulation of RNA-binding proteins and spliceosomal components in response to xenobiotic stress. It is notable that C1-MDB had a significant effect on miRNA processing and chromatin remodeling, altering global gene expression, while C6-MDB had a more discrete effect on spliceosome-mediated RNA splicing events, highlighting a more focused impact on splicing and RNA metabolism.

GO Biological Process enrichment analysis of 702 genes affected (x > 10% in PSI) by CBZ treatment (Supplemental Table 25; Additional Information) reveal critical impacts on DNA modification, RNA processing, and lipid biosynthesis, including significant involvement in DNA alkylation and methylation, pointing to CBZ’s unique influence on genetic stability and epigenetic regulation. CBZ altered splicing for genes involved in RNA processing/splicing, emphasizing a broader impact on post-transcriptional regulation of gene expression compared to the MDB compounds. Notably, RNA-binding and RNA-modification genes like RBM39, TUT4, and SRPK1 showed increased splicing only after CBZ treatment, illustrating the complex selection pressure induced by CBZ. Unlike C6-MDB, which primarily targets splicing regulation, CBZ’s effects span both genetic and metabolic landscapes, indicating a multifaceted impact on cellular physiology. Furthermore, while CBZ and C1-MDB both impact the splicing of RNA processing genes, they diverge significantly in regulating genes involved in lipid metabolism and DNA modification.

Next, a paired KEGG Pathway Analysis of all significantly spliced genes modulated by C1-MDB, C6-MDB, and CBZ was completed, confirming distinct biological impacts of each xenobiotic on both gene splicing and expression (Supplemental Tables 26–34; Additional Materials). The details of the KEGG pathway analysis are discussed in greater detail in the Supplemental Results (Additional Materials), but in general, KEGG analysis reinforced the GO biological process analysis, revealing distinct biological impacts of each xenobiotic on immune response, metabolic processes, and tissue homeostasis. The KEGG analyses highlight a shared emphasis on splicing associated with metabolic regulation, particularly for genes linked to glycerophospholipid metabolism (CHKB, DGKA, LCAT). However, unique splicing responses were also observed, such as C1-MDB’s impact on genes linked to ubiquitin-mediated proteolysis (WWP1, CBLB) and AMPK signaling genes (STK11, TSC2). C6-MDB uniquely modulated the splicing of genes linked to mitophagy and mTOR signaling (PRR5, CAB39L), while CBZ’s influenced the splicing of genes in cell growth/cancer-related (EGF, RPS6KB2, PDGFA) and ErbB signaling (EGF, RPS6KB2) pathways, reflecting a more global impact on cell proliferation and communication, than either MDB compound. These findings highlight the distinct biological processes each xenobiotic affects and the nuanced cellular adaptations mediated by alternative splicing in specific signaling pathways.

While each xenobiotic modulates a discrete set of gene expression and splicing events, we did identify 66 common genes that were comparably spliced across all 3 treatments. GO Biological Process and KEGG pathway analysis of the 66 commonly spliced genes identified mutual alternative splicing patterns among cell growth and signal transduction genes (NCOA1, EGF, RPS6KB2), including genes linked to Rap1 signaling (EGF, CTNND1, PDGFA), and the activation of stress responses (NCOA1, FAM120B; EPB41L5, EPB41, SORBS1). While each xenobiotic uniquely modulates specific gene sets, all universally alter metabolism, stress response, and cell repair processes to some extent, highlighting shared cellular adaptations to xenobiotic exposures (as highlighted in Supplemental Tables 35 and 36; Additional Information).

To gain deeper insights and visualize the relationship between xenobiotic-induced changes in gene expression and alternative splicing, we performed a 3D-clustergram analysis that paired a KEGG pathway analysis for significantly spliced genes (PSI > 10%, FDR < 5%, p < 0.05; from rMATs-turbo; Enrichr), with differential gene expression data obtained from RNAseq. Log2 Fold change data from rSeqDiff was used in this analysis, as it quantified differential gene expression levels for a higher percentage (65%) of significantly spliced genes than DESeq2 (35%; see Supplemental Table 37; Additional Information). Figure 6a presents a 3D-clustergram for C6-MDB treated PRHs, highlighting 20 of the 862 total significantly spliced genes, and their associated biological pathways. Within this 3D landscape it is notable that the majority of genes undergoing splicing modulation after xenobiotic exposure are associated with a decrease in gene expression, as highlighted by the RAB7A gene, which displayed a ~ 12% reduction in splicing (SE event) after treatment, associated with an ~ 53% decrease in total mRNA (Log2 fold change = -1.11). This was in contrast to genes like HRAS, which demonstrated significant splicing increases (RI; 11% increase) linked to an ~ 46% increase in mRNA expression (Log2 fold change = 0.55). The 3D landscape plot for the C6-MDB treatment correlates with the KEGG pathway analysis shown in Supplementary Table 30 (Additional Information), and a 2D-contour plot analysis of the same dataset is provided in Supplemental Fig. 8a (Additional Information) to facilitate data interpretation. Comparable 2D-contour and 3D-clustergram plots for C1-MDB and CBZ treatment groups are also provided in Supplemental Figs. 9 and 10 (Additional Information), and show similar trends, with a preponderance of decreased gene expression and splicing in many commonly modulated pathways.

Fig. 6 — 3D-Clustergram Analysis of Global Alternative Splicing and Differential Gene Expression in Xenobiotic-induced PRHs. To further assess the relationship between gene expression and alternative splicing after xenobiotic exposure, we performed a 3D-clustergram analysis, integrating 2D-KEGG pathway analysis insights with significant splicing data (PSI > 10%, FDR < 5%, p < 0.05) from rMATS-turbo and differential gene expression data from rSeqDiff (Log2 fold change). This analysis is visualized through color-coded plots for expression changes but omits PSI values of significant splicing events. **(a)** For C6-MDB treated PRHs, 862 significant splicing events were detected using rMATS-turbo, and we performed a 2D-KEGG pathway analysis on this data set using the Enrichr website. Here we highlight 20 significantly spliced genes identified by KEGG pathway analysis. Significant splicing events induced by C6-MDB were associated with decreased mRNA expression for genes like RAB7A (~ 53% decrease; Log2 fold change = -1.11), which showed 12% reduced splicing (SE) after treatment. This was in contrast to genes like HRAS, which demonstrated significant splicing increases (RI; 11% increase) in association with a ~ 46% increase (Log2 fold change = 0.55) in mRNA expression. **(b)** Analysis of 66 common genes significantly spliced across all three xenobiotic treatments (C1- and C6-MDB, CBZ) revealed trends similar to C6-MDB alone, with most significantly spliced genes, like the protein synthesis and cell growth regulatory gene RPS6KB2 showing suppressed average expression (27% decrease; Log2 fold change = -0.34), associated with 3 discrete A5SS events across the 3 treatments (44% increase for C1-MDB; 14% increase for C6-MDB, and 11% decrease for CBZ). In contrast, glutathione S-transferase 3 (GSTM3) showed a 27% increase in expression (Log2 fold change = 0.34) across all treatments, correlating with 81% of the 21 splicing events leading to increased splicing diversity. Specifically, 17 splicing events (greater than 10%) showed increased diversity, while 4 events (greater than 10%) showed decreased splicing, with the latter occurring only in the CBZ treatment

Figure 8b presents an aggregated 3D-Clustergram analysis of the 66 common genes that were significantly spliced (PSI > 10%, FDR < 5%, p < 0.05) within all 3 xenobiotic treatments, as highlighted in Supplemental Tables 35 and 36 (Additional Information). This composite assessment of global splicing sensitivity revealed similar trends as the C6-MDB treatment alone, but the averaged magnitude of change was more muted. The expression level of significantly spliced genes (FKTN, BTD, GSTM3, ADH6, RPS6KB2, EGF, PDGFA, NCOA1, SORBS1, CTNND1) were, in general, more likely to be suppressed after xenobiotic treatment, as demonstrated by changes in the RPS6KB2 gene, which was linked to reduced splicing (A5SS; 36% reduction after treatment) and a 21% decrease in expression (Log2 fold change = -0.34). Results for the GSTM3 gene were an exception to the general trend, showing a 27% increase in expression (Log2 fold change = 0.34) associated with reduced splicing complexity (decreased PSI for 15/18 unique events) after treatment. Similar trends were observed with our C1-MDB and CBZ data suggesting that alternative splicing generally varies in conjunction, and in the same direction, with changes in gene transcription. However, some splicing events occur independently of expression level changes, and occasionally, splicing complexity decreases even when gene expression is induced, highlighting the multifaceted nature of splicing regulation.

Alternative gene splicing in the cytochrome P450 gene superfamily

Our lab has historically focused on studying CYP genes and their alternative splicing, which sparked our interest in global splicing mechanisms. This project aimed to scrutinize both targeted and global changes in CYP gene splicing within the complete superfamily of enzymes expressed in the PRH model under xenobiotic stress. Figure 7a and e delineate significant CYP splicing events across these treatments, plotting unique splicing events per gene against the X-axis, where each bar signifies an individual splicing occurrence, with some genes manifesting multiple types. Changes in splicing, represented by -ΔPSI values, indicate either increases (+ value) or decreases (- value) in splicing compared to the control (0.5% DMSO), with five types of splicing events detected by rMATs-turbo showcased in Fig. 7d. Notably, C1-MDB treatment predominantly resulted in reduced splicing, with CYPs 2c13 and 17a1 exhibiting over 20% decreases in splicing variation, contrasting with modest increases in CYPs 2b3, 2c6v1, and 3a2 (Fig. 7a). Of these genes, only Cyp17a1 was suppressed at the mRNA level (~ 20%; log2fold change = -0.32) by C1-MDB treatment.

A similar pattern was observed under C6-MDB treatment (Fig. 7b), where Cyp1a1 showed a significant decrease (~ 26%; A3SS event) in splicing, associated with an ~ 25-fold induction of mRNA (log2fold change = 4.68), and Cyp2b1 showed a 5% reduction in splicing (A5SS event) linked to a 136-fold induction of the Cyp2b1 transcript (log2fold change = 7.09). No significant changes in Cyp2b2 splicing were detected by rMATs-turbo, possibly due to the short (24 bp) length of the variant insertion sequences relative to computational detection limits, despite a robust, 76-fold induction (log2fold change = 6.25) of Cyp2b2 mRNA. For the C6-MDB treatment, reduced splicing complexity was also associated with reduced mRNA expression for Cyp2a2 (-1.2-fold), Cyp4f1 (-1.1-fold) and Cyp27a1 (-1.1-fold) only.

CBZ treatment further corroborated these trends (Fig. 7c), displaying substantial reductions in splice variation for CYPs 2c13, 3a18, and 17a1, with only slight increases in CYPs 2a2 and 3a2 splicing. Here, Cyp2a2 (-1.2-fold), Cyp2c13 (-1.5-fold), and Cyp17a1 (-1.4 fold) showed a modest suppression of gene expression after CBZ treatment, with Cyp3a18 (1.1 fold) and CYP3a2 (6.5-fold) showing modest induction. In general, xenobiotic exposures suppressed CYP splicing diversity across all splicing types, and this was not predictable based on the xenobiotic or the level of individual gene induction or suppression induced. The reduced complexity of alternative splicing observed among multiple CYP gene families (1, 2, 3, 4, 17, 27, 51) after xenobiotic exposure hints that radical oxygen stress within the cell, and/or the expression of discrete, epi-transcriptomic factors may play a larger role in dictating the alternative splicing fate of a pre-mRNA transcript, than its expression level, as partially dictated by nuclear receptor activation (Fig. 7e).

Alternative gene splicing of the NR gene superfamily

This study also explored the intricate landscape of alternative splicing within the nuclear receptor (NR) superfamily and related xenosensors like the arylhydrocarbon receptor (AHR), which partially regulate CYP gene expression after xenobiotic exposure. Figure 8a and e capture the distinct splicing events triggered by each of our 3 treatments, highlighting splicing alterations through -ΔPSI values as shown in Fig. 7. Xenobiotic exposure induced significant modulation in the splicing of several important xenosensors (PXR (Nr1i2), CAR (Nr1i3), and AHR) and classical NRs (LXRα, FXR, LRH-1) along with the circadian rhythm gene BMAL1 (Arntl) (Fig. 8e). Notably, C1-MDB exposure led to ~ 5% decreases in splicing events for genes like LXRα (Nr1h3) which was modestly induced (1.1-fold), and ~ 5% increases and decreases in splicing for genes like FXR (Nr1h4), which was suppressed after exposure (~ 1.1-fold). PXR (Nr1i2) was associated with 3 types of splicing events (SE, A3SS and RI) that were decreased after C1-MDB exposure, and this was associated with a 1.3-fold suppression of PXR mRNA. Larger and more significant splicing events were detected for CAR (Nr1i3), which saw increases in A3SS (10%) and RI (31%) events, and a substantial decrease (24%) in an A3SS event, associated with an ~ 2-fold suppression of CAR mRNA in the treated PRHs. A 65% decrease in a BMAL1 (Arntl) splicing event was also detected, but was not associated with a significant change in mRNA expression, although AHR expression was also found to be suppressed (1.6-fold) after exposure to C1-MDB (Fig. 8a).

Fig. 8 — rMATs Analysis of Alternative Gene Splicing of the NR Gene Superfamily. Our study also examined gene splicing events in the nuclear receptor (NR) superfamily, and the arylhydrocarbon Receptor (AHR), a member of the basic Helix-Loop-Helix (bHLH) PAS family of transcription factors, across PRH treatments with **(a)** C1-MDB, **(b)** C6-MDB, and **(c)** CBZ. The figure shows unique splicing events per treatment, with bars indicating individual events per gene, some showing multiple splicing alterations. Changes in splicing, marked by -ΔPSI values, highlight increased (+) or decreased (-) splicing compared to control (0.5% DMSO). **(d)** Five splicing event types detected by rMATs Turbo are depicted. **(e)** Changes were observed in 3 xenosensors (PXR, CAR, AHR), 3 NRs (LXRα, FXR, LRH-1) related to metabolic regulation, and the circadian rhythm regulator (BMAL1). With C1-MDB treatment, most significant splicing events (p < 0.05; FDR < 5%) were decreased, though significant increases in A3SS (10%) and RI (31%) events were seen for Nr1I3 (CAR), with a notable decrease (24%) in another A3SS event. The circadian gene Arntl (BMAL1) experienced a significant (65%) decrease in MXE usage with C1-MDB only. C6-MDB treatment revealed fewer significant events, but highlighted a 4–8% increase in SE events for the AHR and a 21% increase in a RI event for Nr1I3 (CAR), with Nr1I2 (PXR) showing an 8% decrease in RI events. CBZ treatment confirmed that xenobiotic exposure generally reduces NR splicing diversity, with 4–5% reductions in SE events, 7% in A5SS, and 11% in RI events across Nr1h4 (FXR), Nr1I2 (PXR), Nr1I3 (CAR), and Nr5a2 (LRH-1)

C6-MDB treatment, on the other hand, showcased reduced splicing (SE; 4%) for FXR (Nr1h4), which had no expression change, and a slight uptick (4–8%) in 2 SE events for the AHR, which was induced 1.6-fold by C6-MDB (Fig. 8b). In addition, an 8% decrease in PXR splicing (Nr1i2; RI event) was detected, associated with 1.2-fold suppression of mRNA, while a 21% increase in CAR splicing (Nr1i3; RI event) was detected for CAR linked to a 1.5-fold induction of mRNA. As shown in Fig. 8c, CBZ treatments further supported the trend for a reduction in splicing diversity for xenosensors and NRs, as evidenced by decreases in SE (4–5%), A5SS (7%), and RI (11%) events across key xenoreceptors, including Nr1h4 (FXR), Nr1I2 (PXR), Nr1I3 (CAR), and Nr5a2 (LRH-1), all of which were suppressed (FXR (Nr1h4; -1.2-fold); LRH-1 (Nr5a2; -1.1-fold), CAR (Nr1i2; -1.3-fold) or unchanged (PXR; Nr1i3) after CBZ exposure.

This comprehensive analysis suggests that xenobiotic stress can influence the alternative splicing landscape of NRs and related xenosensors, and potentially impact their regulatory roles in xenobiotic metabolism, detoxification, and other endogenous systems, such as the circadian rhythm. Notably, the majority of xenobiotic-induced changes in NR splicing correlated directly with changes in mRNA expression levels. However, both FXR and CAR exhibited both increases and decreases in discrete splicing events, despite an overall downregulation of their mRNA transcript levels following xenobiotic exposure.

IGV analysis of Cyp2b2 and Cyp1a1 gene splicing in MDB and CBZ treated PRHs

Another central goal of this project was to validate the ability of computational programs like rSeqDiff and rMATs-turbo to identify and quantify a well-known, gene-splicing events, in the Cyp2b2 gene for example, using Illumina-based RNA sequencing. Neither program was capable of accurately quantifying the relative levels of alternative splicing for the Cyp2b2v variant which uses an alternative 5’ splice site (A5SS) in intron 5 to introduce an additional 8 amino acids into the primary sequence. However, manual curation of this splicing event within our RNAseq dataset was made possible using the Integrative Genomics Viewer (IGV), which was used to generate Sashimi plots of RNA-seq data reads across the exon 5/intron 5 junction in the Cyp2b2 gene (Fig. 9a). We manually quantified splicing rates for the Cyp2b2v using IGV Sashimi plots for C1-MDB (Fig. 9b), C6-MDB (Fig. 9c) and CBZ (Fig. 9d) treated PRHs, and representative plots are shown, highlighting the percent splicing inclusion (PSI) level for Cyp2b2v within a single biological replicate. Cumulative results of our Sashimi plot analysis of Cyp2b2v expression are shown in Fig. 9e, and show both the total sequencing reads across the splice junction (black axis), and the PSI ratio comparing the total number of variant-to-wild-type reads (red axis) across the exon 5/exon6 junction. We identified remarkable stability in the ratio of Cyp2b2v to wild-type mRNA, which only vacillated between 39 and 41% across the vehicle controls and 3 treatments. The total number of reads is also a proxy for gene induction and coincides with DEG trends observed for the Cyp2b2 gene using DESeq2. For comparison, we also conducted a Sashimi plot analysis of another well-documented splicing event (SE) in the human CYP1A1 gene that results in the conditional skipping of exon 6 (CYP1A1_ΔExon6_variant; [33, 76]). This ΔExon6 event in rat Cyp1a1 was not detected by PCR or using rSeqDiff or rMATs-turbo, however it was visible in the raw RNA-seq data using IGV. Despite limited sequencing reads for Cyp1a1, we detected a 5- to 10-fold increase in ΔExon6_variant mRNA levels in MDB-treated PRHs, with no exon 6 skipping observed in CBZ-treated cells (Fig. 9f). These findings underscore the specificity of splicing events, where some splice variants are constitutive products of normal splicing, while others are inducible and shaped by complex structure-activity relationships involving nuclear receptors and splicing factors, such as SR proteins and epi-transcriptomic machinery.

Fig. 9 — IGV analysis of Cyp2b2 and Cyp1a1 Gene Splicing in Xenobiotic-induced PRHs. This project also aimed to assess the efficacy of existing computational programs (rSeqDiff and rMATs Turbo) to detect the Cyp2b2v splice variant using RNA sequencing. Neither program was capable of accurately quantifying the expression or alternative splicing of the Cyp2b2var variant which uses alternative 5’ splice site (A5SS) selection to extend exon 5 by 24 base pairs. a.) To overcome this computational obstacle, we used the Integrated Genomics Viewer (IGV) to generate Sashimi plots of RNA-seq data reads across the exon 5/intron 5 junction in the Cyp2b2 gene, which were quantified manually from 4 biological replicate RNAseq reads. Representative plots from **b.)** C1-MDB; **c.)** C6-MDB and **d.)** CBZ treated PRHs are shown highlighting reads across the splice junction, and the percent splicing inclusion (PSI) ratio for Cyp2b2v variant. e.) A complete sashimi plot analysis of the A5SS insertion event is shown, highlighting both the average total reads across the splice junction, and the averaged PSI ratio of Cyp2b2 variant to wild-type. The analysis showed a consistent PSI ratio (39–41%) across controls and treatments, suggesting stable or constitutive variant expression before and after exposure. **f.)** We also examined an exon 6 skipping event in rat Cyp1a1. This analysis revealed a 5-to-10-fold increase in the ΔExon6_Variant for C1-MDB and C6-MDB treatments but no change with CBZ, highlighting how splice variants can differ in stability and inducibility. These findings underscore the complexity of splice variant expression, which is influenced in part by structural-activity relationships that dictate interactions with nuclear receptors, splicing factors and related components of the cellular defensome

Network analysis of xenobiotic-induced splicing events and transcription factor crosstalk

The comprehensive impact of xenobiotics on alternative splicing and cellular signaling pathways was further examined using Enrichr’s Transcription Factor Protein-Protein Interaction (PPI) network analysis [59, 64–66, 77, 78]. This analysis focused on commonly spliced genes, genes with significant splicing events (greater than 55% change), and a global view encompassing all significant splicing events (± 10% change). Each network diagram shown in Fig. 10 illustrates the indirect influence of xenobiotics on transcriptional regulators of highly spliced genes, underscoring the complex crosstalk within cellular pathways exposed to xenobiotic stress.

Fig. 10 — Transcription Factor Signaling Networks and Splicing Crosstalk Across Three Xenobiotic Treatments in PRHs. The impact of xenobiotics on splicing and cellular signaling was examined using Enrichr’s Transcription Factor (PPIs) network analysis for commonly spliced genes (top), highly spliced genes (> 55% change; middle), and all significant splicing events (± 10% change; bottom). Each diagram depicts the xenobiotics’ indirect influence on transcriptional regulators, highlighting crosstalk within cellular pathways under xenobiotic stress. **(a)** C1-MDB exposures induced splicing events in genes linked to SP1, HNF1A, NR3C1 (GR), and TP63, implicating integrated metabolic, stress, and developmental regulation. **(b)** Commonly spliced genes linked to C6-MDB exposure were nearly identical, with a shift from chromatin remodeling (EP300) to immune modulation (ILF2). **(c)** CBZ altered splicing frequency in pathways linked to differentiation (SMAD3), hormonal responses (ESR1; NR3C1 (GR)), and RNA surveillance (UPF1). **(d)** C1-MDB caused significant splicing changes in genes linked to the AHR, ERG, ESR1, and related networks (CLOCK, SMAD3), highlighting coordination among xenobiotic sensing, hormone regulation, and circadian timing. **(e)** C6-MDB modulated large splicing changes in pathways that influence xenobiotic defense (AHR, PXR, FOXA2, BCL3) and lipid metabolism (RARG, HNF4A, PPARD, PPARGC1A, ESRRA, CIITA). While both MDBs engaged AHR genes, C1-MDB primarily interacts with ESRs, whereas C6-MDB connects with PXR, PPARD, and HNF4A. **(f)** Highly spliced genes induced by CBZ are linked to chromatin remodeling (CHD1, SMARCA4), transcriptional regulation (TP63), and the immune response (NFKB1). **(g)** Globally, C1-MDB induced diverse splicing changes across chromatin remodeling (SMARCC1, PML), histone modification (SETDB1, RNF2), hormonal signaling (ESR2, RARA, RXRA) and transcriptional regulation networks (YAP1, FOXP3, PPARGC1A). **(h)** C6-MDB induced global splicing changes in pathways linked to stress response (TP53, NFKB1, FOS), immune regulation (FOXP3), metabolism (PPARGC1A, SREBF1), and cell growth (MYC). **(i)** CBZ exposures altered global splicing in pathways linked to transcriptional regulation (TBL1XR1, ZBTB33), hormonal signaling (NR3C1, ESR1), cell cycle control (TP53, MYC) and signal transduction (SMAD4, SMAD2, SMAD3)

Figure 10a shows how exposure to C1-MDB induced multiple splicing events within genes closely associated with SP1, HNF1A, NR3C1 (Glucocorticoid receptor; GR), and TP63, revealing a coordinated adaptation mechanism integrating metabolic stress and developmental signals in response to xenobiotic exposure. A similar pattern of crosstalk was observed with C6-MDB exposure (Fig. 10b), with a shift from chromatin remodeling (EP300; for C1-MDB), to immune modulation (ILF2; for C6-MDB). This subtle shift in network integration illustrates the nuanced alterations in gene splicing that different xenobiotics can provoke, affecting chromatin architecture and immune response dynamics in distinct ways.

Figure 10c examines CBZ’s effects of transcriptional regulators, revealing altered splicing in pathways pivotal for cellular differentiation (SMAD3), hormonal responses (ESR1; GR), and RNA surveillance (UPF1). These findings highlight the broad spectrum of cellular processes influenced by xenobiotic exposure, spanning from cell differentiation and hormonal signaling to RNA stability. However, CBZ’s unique impact on RNA surveillance pathways (UPF1), set its actions apart from the broader, metabolic and defense responses triggered by MDB compounds.

Further network analysis (Fig. 10d) shows significant splicing alterations, induced by C1-MDB exposure, in genes linked to AHR, ERG and ESR1 signaling, and associated networks such as CLOCK and SMAD3, emphasizing the interplay between xenobiotic sensing, hormone regulation, and circadian rhythm. Figure 10e reveals how the large splicing changes (> 55%) induced by C6-MDB are linked to pathways governing xenobiotic defense (AHR, PXR, FOXA2, BCL3) and lipid metabolism (RARG, HNF4A, PPARD, PPARGC1A, ESRRA, CIITA). A differential engagement of AHR-responsive genes was also detected among the two MDB treatment groups, with C1-MDB interacting predominantly with estrogen receptors (ESRs) and C6-MDB with classical xenosensors like PXR, PPARD, and HNF4A, highlighting how discrete, xenobiotic defense mechanisms are modulated, or fine-tuned, by the nature of the xenobiotic encountered. In contrast, Fig. 10f shows CBZ-induced splicing changes linked to chromatin remodeling (CHD1, SMARCA4), transcriptional regulation (TP63), and the immune response (NFKB1), indicating substantial reprogramming of genetic expression in response to CBZ.

Figure 10g and i elaborate on the diverse splicing changes induced globally by all 3 xenobiotics across multiple pathways, including changes in chromatin remodeling, histone modification, hormonal signaling, transcriptional regulation, stress response, immune regulation, metabolism, and cell growth. As shown in Fig. 10g, C1-MDB primarily affects chromatin remodeling (SMARCC1, PML), histone modification (SETDB1, RNF2), hormonal signaling (ESR2, RARA, RXRA) and transcriptional regulation networks (YAP1, FOXP3, PPARGC1A). C6-MDB had a greater impact on stress response (TP53, NFKB1, FOS), immune regulation (FOXP3), metabolism (PPARGC1A, SREBF1), and cell growth (MYC) (Fig. 10h). CBZ uniquely alters global splicing in pathways linked to transcriptional regulation (TBL1XR1, ZBTB33), hormonal signaling (NR3C1, ESR1), cell cycle control (TP53, MYC) and signal transduction (SMAD4, SMAD2, SMAD3), highlighting distinct pathways of influence compared to the MDB compounds. The unique nature of signaling associated with MDBs and CBZ underscore the diverse mechanisms by which xenobiotics influence cellular functions. This diversity arises because each xenobiotic differentially induces expression and splicing of xenosensors and defensome genes, uniquely perturbing the chemical defense system. Xenobiotic disruptions of these key regulatory networks may compromise the metabolic feedback loops needed to safely eliminate xenobiotics, particularly in complex mixtures, leading to varied cellular outcomes that can promote or drive environmental diseases.

Discussion

Xenobiotic compounds can significantly alter cellular function through mechanisms like differential gene expression and alternative splicing. Our data demonstrate that extending the alkyl side chain from one to six carbons on the methyldibenzo-p-dioxin (MDB) molecule qualitatively transforms its gene expression and splicing pattern, suggesting an increased role for the aryl hydrocarbon receptor (AHR) in mediating the effects of C6-MDB, as compared to C1-MDB. Additionally, the gene expression and splicing profile of C6-MDB, while similar to that of carbamazepine (CBZ), reveals distinct characteristics pointing towards a greater involvement of the constitutive androstane receptor (CAR), in shaping its unique responses. To explore these differences in xenobiotic signaling and xenosensor crosstalk, we employed both endpoint PCR and RNA sequencing analysis, as some splice variants, such as Cyp2b2var, are challenging to detect with certain splicing analysis software despite being visible in IGV.

The discovery of the Cyp2b2v splice variant, with an eight-amino acid addition, highlights the complexity of spliceosome activity and its sensitivity to subtle genomic changes. Computational modeling of the rat CYP2B2 protein, through homology with rabbit CYP2B4 [79], suggests that modifications to the H-I loop domain may alter the enzyme’s tertiary structure, impacting its interactions with substrates, membranes, or key redox partner proteins such as cytochrome P450 oxidoreductase (CYPOR) (Supplemental Fig. 1; Additional Materials). This structural plasticity underscores how a single genomic sequence can yield diverse, functional protein output, raising important questions about the Cyp2b2v variant’s tissue-specific roles in metabolic pathways and its response to xenobiotics, including MDB-based pharmaceuticals (e.g. paroxetine [80]), and emergent contaminants (ECs) like 1,4-dioxane [81, 82].

Endpoint PCR analysis has been invaluable in revealing baseline and induced expression levels of Cyp2b1, Cyp2b2, and the Cyp2b2v variant, offering insights into the nuanced regulation of these genes under xenobiotic stress (Fig. 2). The differential expression patterns observed among MDB compounds and CBZ not only underscore the variant-specific induction mechanisms and suggest a broader network of transcriptional regulation influenced by xenobiotic exposure. RNAseq analysis further transcriptional reprogramming in response to xenobiotic challenges, with unique and shared gene expression patterns across different compounds (Figs. 3 and 4). These findings establish a foundation for defining xenobiotic exposure signatures within the transcriptome, highlighting the specificity of cellular adaptation mechanisms to chemical stress and the potential for biomarker development.

Our examination of global, xenobiotic-induced alternative splicing events across the hepatocyte transcriptome (see Fig. 5) reveals a dynamic regulatory layer involving multiple signaling pathways and networks, including common xenosensors (AHR, PXR, CAR, FXR, LXR), cellular splicing factors (e.g. SRS Factors (SRSF1, SRSF2, SRSF7); Supplemental Tables 23–25, 29; Additional Materials) and epitranscriptomic machinery (e.g. METTL3, METTL14, EIF4G1, HNRNPU, HNRNPH1, and HNRNPC; Supplemental Tables 23–25, 29 and 36; Additional Materials). These networks converge to control mRNA splicing and expression, with xenosensors, splicing factors, and epitranscriptomic machinery working together to shape transcript fate. Alternative splicing within these networks modulates xenobiotic effects on global gene splicing and translation, reflecting a responsive system where environmental cues intricately shape genome-encoded cellular responses.

Alternative splicing is increasingly understood to be influenced by various genetic and epigenetic factors including age [83], diet [84], heavy metal exposure [85], disease status [3, 32, 86], the differential expression of splicing factors like SR proteins [87, 88], histone modifications [44, 89, 90], and epitranscriptomic mechanisms such as METTL3-mediated m6A modifications [91]. While alternative splicing patterns are inherent to the genome, xenobiotic exposures can further modify these patterns, with changes that may persist long-term, especially under chronic exposure. Together, these factors shape splicing outcomes, affecting cellular differentiation, tumorigenesis, and responses to environmental cues like hypoxia, highlighting the complexity and adaptability of splicing regulation in health and disease.

The 3D-clustergram analysis in Fig. 6a illustrates the distinctive effects of C6-MDB treatment on gene expression and alternative splicing in primary rat hepatocytes, revealing significant changes in the splicing landscape after exposure. For example, Rab7a, a key regulator of endocytic trafficking and lysosomal degradation, shows substantial downregulation and reduced splicing after treatment, indicating potential disruptions in autophagic and endocytic pathways essential for cellular homeostasis. Conversely, HRAS, involved in cell growth and differentiation via the MAPK/ERK pathway, exhibits increased expression and splicing, which may enhance proliferative and metabolic pathways linked to cell growth and cancer progression. This 3D visualization captures the complex interplay between gene expression and splicing, providing a comprehensive view of how a single xenobiotic or chemical mixture can reshape the cellular splicing landscape and rewire transcriptional output.

The targeted analysis of CYP gene splicing (Fig. 7) highlights the diverse splicing landscape induced by xenobiotics, revealing both broad and specific alterations in splicing patterns. The distinctive effects of MDB compounds and CBZ on CYP splicing diversity reflect their compound-specific regulatory influences and suggest a role for post-transcriptional modifications in dictating the bioavailability and functional activity of metabolic enzymes. Paired analysis of the NR superfamily and related xenosensors (Fig. 8) revealed similar complexity, showing that many key transcriptional regulators also undergo changes in expression and splicing during xenobiotic-induced cell stress. These splicing changes, which do not always correlate with shifts in mRNA expression levels, emphasize adaptive regulatory mechanisms in the defensome, that fine-tune absorption, distribution, metabolism, and excretion (ADME) processes.

The Integrative Genomic Viewer (IGV; Fig. 9) enables detailed manual curation of splicing events, providing both a visual and quantitative assessment of splicing levels in RNAseq data. While RNAseq effectively captures reads necessary to detect small splicing events, many bioinformatics tools, including rMATs-turbo, apply thresholds (typically 50 bp) that optimized for global analysis, making it difficult to detect smaller insertions or deletions, such as the 25 bp insertion in Cyp2b2v. IGV’s ability to visually confirm these small events highlights its value in supplementing automated bioinformatics pipelines, allowing detection of variants that other tools may overlook. As Shen et al. (2014) noted, rMATs is tailored for larger, more frequent splicing events, which accounts for our inability to quantify Cyp2b2v expression levels using automated detection alone [69]. IGV can identify discrete splicing events in genes like Cyp2b2 that may be too small for accurate detection by programs like rMATs-turbo. By combining IGV’s precision with the scalability of automated tools, we can capture splicing events of all sizes, bridging gaps in strictly automated workflows. Because Sashimi plot analysis in IGV remains largely manual, fully automating this process for comprehensive transcriptome-wide analysis is an ongoing challenge. The future development of hybrid tools that integrate quantitative splicing data with automated Sashimi plot generation would greatly enhance high-throughput splicing analysis, especially for differentially expressed genes. By combining IGV with automated splicing tools, both global patterns and smaller, curated variants can be accurately detected, helping to minimize gaps in splicing analysis.

This approach is particularly useful for Illumina-based, paired-end sequencing, where small splicing events may be missed. Sashimi plot analysis with IGV improves detection of xenobiotic-induced splicing variability, capturing both large-scale and subtle changes. This method can reveal stable, naturally-occurring variants (e.g., Cyp2b2v) with expression ratios (variant/wild-type) that are less influenced by chemical exposure, as well as xenobiotic-inducible variants (e.g., Cyp1a1-ΔExon6), potentially dependent on xenosensor bioactivation (e.g., AHR), cell-specific splicing factors, and regulatory mechanisms such as epigenetic factors (e.g., histone modifications or DNA methylation) and epitranscriptomic modifications like METTL3-mediated m6A, which are all crucial for processing splice-variant transcripts.

The transcription factor network analysis shown in Fig. 10 illustrates the extensive signaling networks and crosstalk elicited by xenobiotic treatments, impacting splicing events across numerous genes. These networks reveal the complexity of cellular responses to discrete xenobiotics, integrating metabolic, stress-related, and developmental signals through coordinated transcription factor dynamics. Our findings illustrate the intricate web of transcription factor signaling networks and splicing-related crosstalk elicited by xenobiotic treatments in primary rat hepatocytes. The observed splicing events and their association with distinct xenosensor networks showcase the complex interplay of cellular mechanisms that support homeostasis under xenobiotic stress, underscoring the adaptive capacity of cellular systems to chemical challenges from the environment.

In conclusion, our network analysis spotlights the complex regulatory mechanisms underlying splicing in response to xenobiotic challenges, with nuclear receptors, splicing factors, oxidative stress, and epitranscriptomic modifiers as key influencers. The unique splicing patterns elicited by C1-MDB, C6-MDB, and CBZ reveal the diverse biological processes and pathways activated by each xenobiotic exposure. Future experiments to clarify how xenobiotics affect gene splicing and protein translation should include analyses of differential expression in splicing factors, assessments of oxidative stress markers, and investigations into both histone and RNA modifications, such as m6A methylation [92]. These approaches will identify specific epigenetic factors that drive global splicing and expression changes relevant to human disease progression or environmental adaptation.

This study also supports the concept of xenobiotic-induced transcriptomic biomarkers. Visualizing gene expression and splicing changes in a 3D landscape (Fig. 6) enables direct links between splicing-related topologies and specific classes of xenobiotics, offering a diagnostic tool for exposure signatures. These biomarkers could help assess lifestyle or occupational safety hazards, while also clarifying adverse outcome pathways (AOPs) associated with splicing changes over time. Looking forward, integrating spatial transcriptomics and single-cell RNAseq will uncover more intricate regulatory networks and cell-type-specific splicing events, expanding the reach and impact of high-resolution sequencing.

Our findings indicate a shift from random splicing in untreated cells to more regulated patterns under chemical exposure, shedding light on how xenobiotics regulate the spliceosome. This focused splicing regulation, combined with xenobiotic-specific nuclear receptor binding and transcriptional regulation, emphasizes the complex interplay shaping adaptive cellular responses to xenobiotics. Future studies on crosstalk among xenosensors, splicing factors and epitranscriptomic modifiers could reveal new strategies for cells to maintain homeostasis, potentially identifying new therapeutic targets to mitigate the adverse effects of xenobiotic exposure.

This study reveals important aspects of xenobiotic-induced alternative splicing; however, further research on the protein expression of splice variants is warranted. Endpoint PCR analysis (Fig. 2 and Supplemental Fig. 5; Additional Materials) confirmed RNA-seq findings, showing that the Cyp2b2v variant maintains a consistent baseline expression at approximately 40–100% of wild-type Cyp2b2 levels across all controls and treatments. These results suggest a stable expression profile for Cyp2b2v, with potential modulation by specific xenobiotic exposures. However, detecting Cyp2b2v splice variant expression in rat hepatocyte cultures at the protein level proved challenging due to low protein yield. Preliminary mass spectrometry detected a mutant peptide with limited signal strength (data not shown), stressing the need for a targeted proteomics approach that can more reliably quantify these splice variants and bridge transcript and protein levels, as RNA-based findings may not fully capture protein dynamics. Meanwhile, RNA-seq analysis showed that Cyp2b2v’s ratio to wild-type remained stable across treatments, while Cyp1a1 (ΔExon6) was significantly induced, suggesting AHR activation, especially with C6-MDB exposure. Consistently, Fig. 10 reveals that MDB compounds, particularly C6-MDB, selectively activate AHR networks, linking xenobiotic sensing to lipid metabolism and immune response pathways. This highlights AHR’s pivotal role in orchestrating cellular adaptations to environmental stressors through the modulation of alternative splicing, underscoring how specific xenobiotics uniquely shape splicing landscapes and transcriptional responses that may drive adaptive or pathogenic outcomes.

To deepen our understanding of splicing, assessing the translational fate of splice variant transcripts is essential to uncover additional layers of crosstalk and gene regulation. While some alternative transcripts may function as non-coding RNAs, the diverse functional landscape of splice variant mRNAs and proteins necessitates both computational and biochemical approaches to uncover their clinical relevance in pharmacogenomics and disease diagnostics [57]. This is particularly crucial for highly spliced genes within the defensome, such as human CYP2C9, where single nucleotide polymorphisms (SNPs) can trigger cryptic alternative splicing events that may influence drug-drug interactions (DDIs) and disease susceptibility [93, 94]. Gene environment interactions, including xenobiotic exposure and epigenetic modifications, can further shape SNP driven splicing. Understanding these influences provides insight into global transcriptome modulation by xenobiotics and highlights the role of alternative splicing in adaptive cellular responses.

Methods

Synthesis of 4-n-Alkyl-methylenedioxybenzenes

Methylenedioxybenzene and 4-n-methyl-methylenedioxybenzene were purchased from Aldrich Chemical Co. and purified by vacuum distillation. 4-n-hexyl methylenedioxybenzene was synthesized by a two-step reaction procedure starting with piperonal and n-hexyl-alkyl halide species joined first in a Grignard reaction followed by reduction of the alcohol product with hydrogen and palladium on activated charcoal. An excess of n-alkyl-halide (hexyl; 0.0423 moles) was reacted with 0.04115 moles of magnesium in the presence of anhydrous ether. The alkyl halide dissolved in 25 mL of anhydrous ether was added dropwise to the reaction flask containing the magnesium turnings. The reaction was complete when all the magnesium turnings had been dissolved; heat was applied to the mixture to induce the incorporation of any undissolved reactant. This reaction yielded a Grignard intermediate. Piperonal (0.04115 moles) dissolved in 20 mL of anhydrous ether was then added dropwise to the Grignard intermediate mixture (above) and stirred gently over low heat. The heat source was removed and added, along with 100 mL of saturated aqueous ammonium chloride solution, to a 500 mL Erlenmeyer flask. This reaction mixture (Grignard intermediate plus piperonal, ether, and ammonium chloride) was stirred for 10 min. The hydrolysis mixture was added to a 500 mL separator funnel and the aqueous layer removed. The ether layer was set aside. The aqueous layer was extracted four times with anhydrous ether, saving the ether layers with the primary ether layer. The pooled ether layers were extracted with a saturated sodium chloride solution. The ether solution was dried by the addition of anhydrous magnesium sulfate. The remaining hydroxyl moiety on the alkyl side chain was then reduced by hydrogenation over palladium on activated charcoal in the presence of trace amounts of perchloric acid. The reaction products recovered following reduction were dissolved in anhydrous ether and extracted twice with distilled water. The aqueous extracts were then back-extracted twice with ether and the pooled ether fractions extracted twice with saturated sodium carbonate. The pooled ether phase was then adjusted to pH 7.0, extracted once with distilled water and once with saturated aqueous sodium chloride. The remaining ether phase containing the MDB product was concentrated to dryness by rotoevaporation and the MDB purified from the oily residue by silica gel column chromatography (hexane/ethyl acetate 3:1) and/or vacuum distillation. Structures of final products were confirmed by NMR, and purity of final products was determined to be > 97% as assessed by thin layer chromatography (TLC ) and nuclear magnetic resonance (NMR).

Primary rat hepatocyte (PRH) cell culture

Cryopreserved primary rat hepatocytes were purchased from Sekisui Xenotech (#R1000.H15+, Lot No. 1710122; Kansas City, Kansas). Fresh plated hepatocytes were purchased from BioIVT (Westbury, New York). Cryopreserved cells were thawed in OptiThaw medium (Sekisui Xenotech, Kansas City, Kansas), then spun down at 100 g for 5 min. The medium was aspirated off, then the cells were resuspended in 5 mL OptiPlate plating medium (Sekisui Xenotech, Kansas City Kansas). Cells were diluted 1:10 in trypan blue and PBS, then counted using a hemocytometer. After counting, the cells were diluted to a concentration of 1.5 million cells per milliliter in OptiPlate medium. Three million cells were then plated in each 60 mm collagen I-coated BioCoat plate (Corning Life Sciences, Durham, North Carolina). The cells were placed in an incubator at 37 C and 5% carbon dioxide and allowed to attach for 18 h. The medium was aspirated and overlaid with dilute (1:20 or 0.25 mg/mL) Matrigel extracellular matrix (Corning Life Sciences, Durham, North Carolina) in OptiCulture hepatocyte culture medium (Sekisui Xenotech, Kansas City, Kansas). 24 h following application of overlay, cells were treated with 250 µM C1-MDB, 250 µM C6-MDB, 100 µM carbamazepine (CBZ), or vehicle (VEH; 0.5% DMSO) in OptiCulture media. Representative micrographs of each plate were taken 3 h before harvest (Supplemental Fig. 3; Additional Materials).

Cell viability assay

Alamar Blue HS Reagent (ThermoFisher Scientific, Waltham, Massachusetts) was diluted 1:10 in OptiCulture medium and 3.4 mL added to each plate. The dishes were wrapped in aluminum foil and allowed to incubate at 37° C for two hours. 1 mL of medium containing the reagent was transferred from each dish to a cuvette. Absorbance was measured at 570 nm (test) and 600 nm (reference) using an Ultrospec III spectrophotometer (Pharmacia LKB, Uppsala, Sweden). The absorbance from each wavelength and the molar extinction coefficients of pure resazurin and resorufin were used to calculate the percentage of resazurin reduced to resorufin for each replicate. This number was used to normalize optical density of bands in the PCR gels.

RNA extraction and purification

24 h following exposure, medium was removed and replaced with Cell Recovery Solution (Corning Life Sciences, Durham, North Carolina). Dishes were incubated for 1 h at 4℃ to facilitate dissolution of extracellular matrix. β-mercaptoethanol (Sigma-Aldrich, St. Louis, MO) was added to Buffer RLT (Qiagen, Hilden, Germany) at a concentration of 20 µL/mL, and hepatocytes were lysed with the Buffer RLT and the lysate transferred to 2 mL Eppendorf tubes. RNA was purified according to the RNEasy Mini Kit (Qiagen, Hilden, Germany) instructions, with an on-column DNAse digestion step between Buffer RW rinses 1 and 2 using 80 µL RNAse-free DNAse (Qiagen, Hilden, Germany) per sample to remove any contaminating DNA. The concentration of the purified RNA was determined using the Qubit RNA BR Assay Kit (Thermo Fisher Scientific, Carlsbad, California).

Endpoint PCR

High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, Carlsbad, California) was used to synthesize cDNA using 500 ng RNA as template. Primers specific to Cyp2b1, Cyp2b2, Cyp2b2v, and the housekeeping gene succinate dehydrogenase (SDHA; primer sequences given in Table X1) were obtained from Integrated DNA Technologies (Redwood City, California). For each 50 µL reaction, 25 ng cDNA was used. Primer concentration and annealing temperature were dependent upon target (details given in Table X2) DreamTaq Green 2x Master Mix (Thermo Fisher Scientific, Carlsbad, California) containing DreamTaq polymerase was used to catalyze amplification of the primer targets. 25 µL of each PCR product was combined with 5 µL 50% glycerol and loaded onto a 2% agarose-TAE (tris-acetate-EDTA) gel prepared with 15 µL/100 mL SYBR Safe Gel Stain (Thermo Fisher Scientific, Carlsbad, California). The gel was run for 60 min at 95 V, then imaged using the Azure c600 Imager (Azure Biosystems, Dublin, California). Images were analyzed using FIJI open source software. Raw optical density of each band was calculated using the peak-picking method. Optical density of each CYP450 band was normalized to the housekeeping gene succinate dehydrogenase (SDHA) and the results of the Alamar Blue HS Assay. Graphs and statistical analysis were prepared from this data using Prism 9.4.1 (GraphPad Software, San Diego, California).

Library preparation and RNA sequencing

After initial quantification and quality control (Qubit RNA HS (High Sensitivity) Assay Kit (ThermoFisher Scientific, Waltham, Massachusetts), 200 ng RNA from each sample was submitted to Novogene Bioinformatics Technology Co., Ltd. (Hong Kong, China) for further quality control and library preparation. Analysis of the transcriptome was performed by RNA sequencing using the NovaSeq 6000 PE150 platform which generates (12G raw data per biological replicate sample). An initial bioinformatics analysis, highlighted in Fig. 3a-b and Supplemental Fig. 6a (Additional Materials), was performed with assistance from the Novogene informatics group. All other RNA sequencing analysis was performed by our laboratory at Oregon State University.

Computational modeling of the Cyp2b2 wild-type protein

The crystal structure of rabbit CYP2B4 (5EM4 [79]) was used to prepare a homology model for rat CYP2B2 using the SWISS-MODEL server [95]. Molecular structures were analyzed and imaged using The PyMOL Molecular Graphics System [96].

Computational analysis of differential gene expression and alternative splicing events

Raw sequencing data obtained from Novogene was re-processed using our own bioinformatics pipeline, utilizing FASTP [97], HISAT2 [98, 99], and SAMtools [100]. Differential gene expression data was obtained from Novogene, and validated using the program rSeqDiff, which was also used to quantify differentials in alternative gene splicing among control and treatment groups. A more comprehensive analysis of alternative splicing was subsequently completed using the program rMATs 4.1.2, which offers improved statistical analysis of percent splicing inclusion (PSI) and a false discovery rate (FDR), which allows it to more accurately identify significant splicing events, while discriminating among the 5 major types of splicing events (SE = skipped exon, A5SS – alternative 5’ splice site selection, A3SS – alternative 3’ splice site selection, RI = retained intron, and MXE – mutually exclusive exon usage). Sashimi plot analysis of raw RNAseq data was conducted using the Integrative Genome Viewer (IGV) from the Broad Institute at MIT, using .BAM files obtained from Novogene aligned to the most current version of the rat genome (rn7) obtained from the UCSC repository. Reads across relevant splice junctions were quantified manually and analyzed for PSI in Microsoft Excel.

Statistical analysis

Data are expressed as means ± standard error. Statistical comparisons between control and treatment groups were analyzed using one-way analysis of variance (ANOVA), followed by Tukey’s range test to determine significant differences among means. Significant differences between control and treatment groups were determined to be those with p-values less than 0.05.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1^{(4.3MB, pdf)}

Acknowledgements

We would like to thank Dr. Ed Davis from the Center for Quantitative Life Sciences (CQLS) at Oregon State University for providing computational support for our bioinformatics pipeline.

Author contributions

A.A. designed experiments, developed computational models, conducted bioinformatics analysis of gene expression and splicing for all sequencing data, developed figures and tables, and was a major contributor to writing and editing the manuscript. J.C. conducted xenobiotic exposures on rat hepatocyte, obtained RNA and performed end-point PCR and RNAseq studies, developed figures and made contributions to writing the manuscript. A.J. developed the bioinformatics pipeline for analyzing RNAseq expression and splicing data. P.I. designed experiments, analyzed data and was a major contributor in writing and editing the manuscript. C.M. designed experiments, analyzed data and was a major contributor in writing and editing the manuscript.

Funding

This research was funded in part by the Environmental Health Sciences Center (EHSC), “Pacific Northwest Center for Translational Environmental Health Research” P30 ES030287 (for AA); NIEHS T32 Training Grant “Integrated Regional Training Program in Environmental Health Sciences” T32 ES007060 (for JC); the Agricultural Research Foundation (ARF) at Oregon State University.

Data availability

The dataset(s) supporting the conclusions of this article are available in the NCBI BioProject repository, accessible via the unique persistent identifier BioProject ID: PRJNA1099310.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1^{(4.3MB, pdf)}

Data Availability Statement

The dataset(s) supporting the conclusions of this article are available in the NCBI BioProject repository, accessible via the unique persistent identifier BioProject ID: PRJNA1099310.

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Global transcriptome modulation by xenobiotics: the role of alternative splicing in adaptive responses to chemical exposures

Andrew J Annalora

Jacki L Coburn

Antony Jozic

Patrick L Iversen

Craig B Marcus

Abstract

Background

Methods

Results

Conclusions

Supplementary Information

Introduction

Results

Validation of alternative splicing in the xenobiotic-induced PRH model

Fig. 1.

Fig. 2.

Global differential gene expression profiling of xenobiotic-induced PRHs using RNAseq

Fig. 3.

Fig. 4.

RNAseq analysis of alternative gene splicing in xenobiotic-induced PRHs

Fig. 5.

Fig. 6.

Alternative gene splicing in the cytochrome P450 gene superfamily

Fig. 7.

Alternative gene splicing of the NR gene superfamily

Fig. 8.

IGV analysis of Cyp2b2 and Cyp1a1 gene splicing in MDB and CBZ treated PRHs

Fig. 9.

Network analysis of xenobiotic-induced splicing events and transcription factor crosstalk

Fig. 10.

Discussion

Methods

Synthesis of 4-n-Alkyl-methylenedioxybenzenes

Primary rat hepatocyte (PRH) cell culture

Cell viability assay

RNA extraction and purification

Endpoint PCR

Library preparation and RNA sequencing

Computational modeling of the Cyp2b2 wild-type protein

Computational analysis of differential gene expression and alternative splicing events

Statistical analysis

Electronic supplementary material

Acknowledgements

Author contributions

Funding

Data availability

Declarations

Competing interests

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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RESOURCES

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